pastas.model.Model.get_contribution
===================================

.. py:method:: pastas.model.Model.get_contribution(name: str, tmin: pandas.Timestamp | str | None = None, tmax: pandas.Timestamp | str | None = None, freq: str | None = None, warmup: float | None = None, istress: int | None = None, return_warmup: bool = False, p: pastas.typing.ArrayLike | None = None) -> pandas.Series

   
   Method to get the contribution of a stressmodel.

   :param name: String with the name of the stressmodel.
   :type name: str
   :param tmin: A string or pandas.Timestamp with the start date for the simulation period
                (E.g. '1980-01-01 00:00:00'). Strings are converted to pandas.Timestamp
                internally. If none is provided, the tmin from the oseries is used.
   :type tmin: pandas.Timestamp or str, optional
   :param tmax: A string or pandas.Timestamp with the end date for the simulation period
                (E.g. '2020-01-01 00:00:00'). Strings are converted to pandas.Timestamp
                internally. If none is provided, the tmax from the oseries is used.
   :type tmax: pandas.Timestamp or str, optional
   :param freq: String with the frequency the stressmodels are simulated. Must be one of
                the following: (D, h, m, s, ms, us, ns) or a multiple of that e.g. "7D".
   :type freq: str, optional
   :param warmup: Warmup period (in Days).
   :type warmup: float or int, optional
   :param istress: When multiple stresses are present in a stressmodel, this keyword can be
                   used to obtain the contribution of an individual stress.
   :type istress: int, optional
   :param return_warmup: Include warmup in contribution calculation or not.
   :type return_warmup: bool, optional
   :param p: array_like object with the values as floats representing the model
             parameters. See Model.get_parameters() for more info if parameters is None.
   :type p: array_like, optional

   :returns: **contrib** -- Pandas.Series with the contribution.
   :rtype: pandas.Series















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